This course is an introduction to the stochastic theory of causality (Steyer et al. Springer: 2010), which is a generalization of the theory of causal effects in the tradition of J. Neyman and D. B. Rubin. All designs and models for the analysis are developed for the purpose of learning about conditional and/or average total, direct, and indirect effects. Unlike other courses on the analysis of treatment effects, it uses structural equation modeling (with or without latent variables) instead of analysis of variance techniques, the General Linear Model or related techniques.

This course is a synthesis of different traditions in methodology: Rubin's approach to causality, the Campbellian tradition of quasi-experimentation and internal validity, and structural equation modeling.

Although this workshop does not require experience and knowledge in structural equation modelling (SEM), we do not recommend this workshop as a first introduction to SEM, if the motivation is to have an introduction into SEM. For this purpose, I rather suggest our course "Introduction to latent variable modeling with Structural Equation Models using Mplus" held in April 2009 at the University of Jena. This and other courses are still available in the internet and on DVDs at: http://www.metheval.uni-jena.de/courses.

The course, "Analysis of Total, Direct, and Indirect Effects in Experiments and Quasi-Experiments" aims at those who are interested in data analysis in experimental and quasi-experimental studies involving covariates such as one or several pretests, a discrete treatment variable, and one or several outcome variables.

Data analysis can be done with EffectLite, which is a program developed by Rolf Steyer and Ivailo Partchev, which will be provided to all participants. It analyzes a generalized multivariate analysis of variance and covariance. It creates LISREL and/or Mplus input files, reads and interprets the results, computes some statistics, and produces an output file containing the results that are most important for the analysis of total, direct, and indirect treatment effects. In the univariate case, EffectLite does not assume homogeneity of variances of the outcome variable between groups. In the multivariate case with two or more outcome variables it does not assume homogeneity of covariance matrices of the outcome variables between groups. Furthermore, it allows analyzing mean differences and adjusted means differences - aimed at estimating causal effects - between groups with respect to

several manifest covariates and/or outcome variables

one or more latent covariates and/or outcome variables, and

a mixture of the two kinds of variables.

The covariate(s) may also be qualitative (blocking factors). In this case, we estimate and test average effects for non-orthogonal analysis of variance designs, provided that the covariates are specified as qualitative indicator variables. If the covariates fulfil certain assumptions, the program estimates and tests the conditional and average causal effects.

In the course we will

present the theory of stochastic causality with an emphasis on causal effects

show how to use EffectLite, LISREL, and Mplus for the analysis of total, direct, and indirect effects Theory

Motivation: Simpson's paradox, non-orthogonal ANOVA

The scope of the theory: random experiments

The mathematical structure of causal models: causality spaces

True outcome variables, average and conditional causal effects

Prima facie effects

Sufficient conditions for unbiasedness

The role of randomization and other design techniques and strategies of data analysis Applications

In Lisrel-versions which had compiled before August, 29, 2007 there were a bug in the FIML option for multiple group analyses. Here you can get an updated version of the executable for the student edition of Lisrel 8.8 without this bug. If you wanted to have the updated executable for the full version of Lisrel, please contact the technical support from Scientific Software International, Inc.